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Bayesian Machine Learning
This page contains resources about Bayesian Inference' '''and' Bayesian Machine Learning.' Bayesian Networks ''do not necessarily follow Bayesian approach, but they are named after Bayes' Rule. Subfields and Concepts * Bayesian Optimization * Variational Bayesian Inference * Bayesian Linear Regression * Probabilistic Matrix Factorization * Bayesian Nonparametrics * Bayesian Parameter Estimation * Bayesian Decision Theory * Bayesian Multitask Learning * Bayesian Model Averaging * Bayesian Reinforcement Learning * Bayesian Occam's Razor * Bayesian Model Comparison ** Bayesian information criterion (BIC) Online Courses Video Lectures * Bayesian Learning by Zoubin Ghahramani - VideoLectures.Net * Graphical modelling and Bayesian structural learning by Peter Green - VideoLectures.Net Lecture Notes * CSC 2541: Topics in Machine Learning: Bayesian Methods for Machine Learning by Radford Neal * CSE 515T: Bayesian Methods in Machine Learning by Roman Garnett * Advanced Statistical Machine Learning by Stefanos Zafeiriou Books and Book Chapters * Davidson-Pilon, C. (2015). Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference. Addison-Wesley Professional. * Conrady, S., & Jouffe, L. (2015). Bayesian Networks and BayesiaLab: A Practical Introduction for Researchers. BayesiaLab USA. * Koduvely, H. M. (2015). Learning Bayesian Models with R. ''Packt Publishing. * Theodoridis, S. (2015). "Section 15.3: Bayesian Networks and the Markov Condition". ''Machine Learning: A Bayesian and Optimization Perspective. Academic Press. * Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. (2014). Bayesian data analysis (Vol. 2). Boca Raton, FL, USA: Chapman & Hall/CRC. * Nagarajan, R., Scutari, M., & Lèbre, S. (2013). Bayesian Networks in R. Springer, 122, 125-127. * Barber, D. (2012). Bayesian Reasoning and Machine Learning. Cambridge University Press. * Duda, R. O., Hart, P. E., & Stork, D. G. (2012). Pattern Classification. John Wiley & Sons. * Murphy, K. P. (2012). "Chapter 10: Directed graphical models (Bayes nets) ". Machine Learning: A Probabilistic Perspective. MIT Press. * Koller, D., & Friedman, N. (2009). "Chapter 3: The Bayesian Network Representation". Probabilistic Graphical Models. MIT Press. * Darwiche, A. (2009). Modeling and reasoning with Bayesian networks. Cambridge University Press. * Bishop, C. M. (2006). "Section 8.1: Bayesian Networks". Pattern Recognition and Machine Learning. Springer. * MacKay, D. J. (2003). "Chapter 37: Bayesian Inference and Sampling Theory". Information Theory, Inference and Learning Algorithms. Cambridge University Press. * Mitchell, T. M. (1997). "Chapter 6: Bayesian Learning". Machine Learning. McGraw Hill. * Pearl, J. (1988). "Chapter 2" Bayesian Inference". Probabilistic Reasoning in Intelligent Systems. Morgan Kaufmann. * Jensen, F. (1996). An Introduction to Bayesian Networks. Springer. Scholarly Articles * Ghahramani, Z. (2015). Probabilistic machine learning and artificial intelligence. Nature, 521(7553), 452-459. Tutorials * Heckerman's Bayes Net Learning Tutorial * A Brief Introduction to Graphical Models and Bayesian Networks * A brief introduction to Bayes' Rule * An Introduction to Graphical Models by M. Jordan * Bayesian Modelling in Machine Learning: A Tutorial Review * Bayesian Methods for Machine Learning - NIPS 2004 * Bayesian Machine Learning by Ian Murray * Bayesian Machine Learning by Zoubin Ghahramani * Dynamical Systems, Stochastic Processes and Bayesian Inference - NIPS 2016 workshop Software * Bayesian Probabilistic Matrix Factorization - MATLAB * Bayesian Modeling and Monte Carlo Methods - MATLAB * Bayesian Methods for Hackers - Python * Infer.NET - Developed by Microsoft Research * OpenBUGS - Bayesian Inference Using Gibbs Sampling See also * Probability Theory * Information Theory * Monte Carlo Methods * Stochastic Processes and Random Fields * Linear Dynamical Systems Other Resources *Bayesian machine learning - Metacademy *Bayesian machine learning - Introduction *Bayesian machine learning - FastML *Are "Bayesian networks" Bayesian? - No, Bayesian and Frequentist approaches can both be used. Category:Machine Learning